Journal of Insect Conservation 5: 87–97, 2001. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.
Effects of vegetation and soil on species diversity of soil dwelling Diptera in a heathland ecosystem Luc De Bruyn1,2 , Sofie Thys1 , Jan Scheirs1 & Ron Verhagen1 Evolutionary Biology group, Department of Biology, University of Antwerp (RUCA), Groenenborgerlaan 171, 2020 Antwerpen, Belgium 2 Institute of Nature Conservation, Kliniekstraat 25, 1070 Brussel, Belgium (Tel.: xx-32-3/558.18.10; Fax: xx-32-3/558.18.05; e-mail:
[email protected])
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Received 17 July 2000; accepted 8 January 2001
Key words: species diversity, soil factors, vegetation, heathland, Diptera
Abstract The role of vegetation and soil factors on the biodiversity of two soil dwelling, saprophagous, fly families (Sphaeroceridae and Lonchopteridae) in a heathland ecosystem were investigated. The fly community is primarily affected by soil humidity and the amount of organic matter while the vegetation structure and species composition only indirectly influence the fly communities. There was no correlation between plant species richness and the fly diversity indices. Based on our results and literature data, we hypothesise that the direct effects of the vegetation is more evident for herbivorous insects than for species that do not feed on plants. The investigated families show a clear response to microhabitat differences in the soil factors, which makes them promising indicators for soil health and as tool for monitoring environmental changes. Introduction It has often been assumed that invertebrate communities are primarily dependent upon the vegetation species composition and structure (Curry 1987) and that management practice for the vegetation should therefore be of equal benefit to the invertebrate communities (Panzer & Schwartz 1998). Other studies, however, showed that at least in some invertebrate groups this is not the case (McFerran et al. 1994; Sanderson et al. 1995). In recent decades, the conservation of insects has received increasing attention, not only because they are worth conserving, but also because some insect groups have been shown to be particularly good bioindicators which react very quickly to environmental alterations. However, the basic knowledge on habitat specificity, necessary to construct such a predictive system, is still scarce, and in most groups even absent (Lobry de Bruyn 1997; van Straalen 1997). Heathland is characterised by large open areas and acidic, nutrient poor, sandy soils. These characteristics
place particular demands to the inhabiting fauna and flora (Stubbs & Fry 1991). As a consequence, many heathland species are specialised and their occurrence is closely linked to the habitat. While most heathland sites have a relative small variety of plants and vertebrates, they are of immense importance to insects (Stubbs & Fry 1991; Usher 1992). Heathland has become an increasingly endangered habitat in Europe. Heathland fragmentation, disappearance and deterioration are such that several of these specialised species have become threatened and ultimately even appeared on the red list of one or more countries. Insects have a tremendous range of ecological roles. Insect detrivores exhibit a major influence on ecosystem functions through impact on nutrient cycling as well as in the processing of decaying vegetation (Seastedt & Crossley 1984; Miller 1993). In the present study we analyse the habitat specificity and diversity of two soil dwelling Diptera families that are constituents of the soil-borne insect fauna, viz. Sphaeroceridae and Lonchopteridae.
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Sphaeroceridae, or lesser dung flies, consists of very common to rather rare, small to very small flies. They are generally saprophagous. The larvae develop in a wide range of decaying organic matter such as dung (mainly from mammals), carcasses of animals, refuse heaps, grass cuttings, etc. (Pitkin 1988). Although they prefer humid conditions, Sphaeroceridae can be found in practically all kinds of habitats. Most species are fully winged, but many of them rarely fly (Pitkin 1988). Lonchopteridae are small flies with typically narrow pointed wings. They commonly occur in damp places with thick leaf carpets, where the larvae live in decaying vegetable matter (Smith 1969). As in the family Sphaeroceridae, the adult flies mainly crawl around on the soil, under the vegetation and rarely fly (B¨ahrmann & Bellstedt 1988). The aim of the present study is to assess the habitat affinities of the two Diptera families and to examine how these affect the fly community. More specifically, we test whether the vegetation and/or soil characteristics are good predictors of the fly diversity. Study area The study area was situated in the nature reserve ‘Groot Schietveld’ on the municipal territory of Brecht, Belgium. This area (2500 ha) consists mainly of a large open heathland surrounded by different types of woods. Different equally sized study plots (25 m2 ) were selected in two study sites. The first site is a dune slope (±500 m2 ) oriented to the south. From the top to the base of the dune, five different vegetation strips were selected as sample plots. The first (P1), at the top of the dune, consisted of large open fragments of bare sand, surrounded by a dense Calluna vulgaris vegetation on a dry soil. The second (P2) was placed in the homogeneous C. vulgaris vegetation surrounding the open spaces. The third (P3) was situated lower in a transition zone of C. vulgaris and Erica tetralix, while the fourth (P4) plot was placed in a homogeneous E. tetralix zone. Finally, the fifth (P5) was situated at the bottom of the dune, in a Molinia caerulea vegetation, bordered by a fen. The second study site was an open area (±18 200 m2 ). The vegetation had a mosaic structure and the different vegetation types occur more or less as isolated patches. The following six sample plots were selected: one dominated by Erica tetralix (P6), one patch with a mixed vegetation of C. vulgaris and E. tetralix (P7), a monotypic Molinia caerulea vegetation (P8), a plot situated in an E. tetralix vegetation covered by Myrica gale (P9), one plot at the border
of a fen (P10) and finally one plot at the edge of the site where the heath is overgrown by the mixed wood vegetation (P11).
Methods Trapping As pointed out by Usher (1990), coloured water traps constitute an excellent method to assess Diptera communities in heather moorland. We used two (white and yellow) coloured water traps in each plot to collect the flies (diameter 19 cm, depth 6.5 cm). They were filled with a 0.04% formalin solution and a few drops of detergent to reduce surface tension. Usher (1990) already showed that traps placed close to the ground captured most Diptera. Sphaeroceridae and Lonchopteridae are both closely tied to the soil. Ven & De Bruyn (1991) showed that some Sphaerocerid species are only caught by pitfall traps while they were absent in different coloured water traps and Malaise traps placed on the soil surface. Therefore, our water traps were dug into the ground with the top of the trap at ground surface level. The traps were installed at the centre of the sample plots to avoid edge effects. They were emptied at weekly intervals from 29 April until 23 December 1995. Each time, the placement of the traps was changed within the plot. Environmental variables The vegetation of each of the 11 plots was sampled with four 2 × 2 m quadrats arranged in a square. The cover of each species was estimated as the average percentage cover of the four 4 m2 quadrats. Nomenclature is according to De Langhe et al. (1983) and Touw and Rubers (1989). In addition, the cover of the different vegetation layers (moss, herb, bush and tree) was estimated irrespective of the constituent plant species, as a measure of the structural composition of the habitat. Small trees were recorded as belonging to the bush layer. The ‘dry’ mosses (e.g. Polytrichum piliferum Hedw.) and ‘wet’ mosses (Spagnum species) were treated as different groups because of their different ecological demands. As a fifth structural habitat parameter, we measured the depth of the litter layer. Soil sampling and chemical analyses were according to Allen (1989). We took five 10 cm deep soil samples in each plot. Soil moisture was measured by weighing wet and dried (105◦ C) samples. After removal of small stones and roots, these samples were sieved (mesh
Relationship between vegetation, soil and fly diversity width 2 mm) and mixed to obtain one mixture sample per plot. Soil acidity was measured on fresh material. Organic matter content (carbon content estimated by loss-on-ignition method) was assessed on dried (105◦ C) soils. Data analysis For each site, species richness and diversity were measured. Species richness was obtained from the number of species which occurred at the site. There are several different indices of species diversity. Because literature reports contradictory recommendations, we followed Magurran (1988) and Krebs (1989) and calculated the Shannon–Wiener (SW) and inverse Simpson (S) diversity indices and α of the logarithmic series. All three measures were obtained with BIODIV 4.1 (Baev & Penev 1993). The three higher mentioned diversity measures were strongly correlated (all r > 0.908; p < 0.001). Therefore we will only report the Shannon–Wiener index. It is the most popular index in literature, and therefore facilitates comparison with other studies. The previous diversity indices only take the number and frequencies of the species into account. They assume that all species at a site, within and across systematic groups, contribute equally to its biodiversity. The functional aspect of species diversity measurement is strengthened by incorporating differences between species as a component of diversity (Cousins 1991; Vane-Wright et al. 1991). Species that are taxonomically more distinct will be expected to make a larger contribution to some overall measure of diversity because they contribute different ‘features’ (Faith 1992). Therefore we calculated the avalanche diversity index (Ganeshaiah et al. 1997). Because the phylogenetic relationships between sphaerocerid and lonchopterid flies are not fully solved yet, we incorporated taxonomic distance in a crude way by assuming the distance as one when species belong to the same genus, two when they differ at the genus level, three when they differ at the subfamily level, and four when they belong to different families. Relationships between the diversity measures and the habitat parameters were assessed by multiple regression techniques using proc REG in SAS 6.12 (SAS 1989). Because species richness consists of count data, a Poisson regression with log-link was performed using proc GENMOD in SAS 6.12 (SAS 1996). Nonlinear regression was performed with proc NLIN in SAS 6.12 (SAS 1989). Model building was based on stepwise and backward selection, and the Cp -criterion
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(Crawley 1993; Neter et al. 1996). For highly correlated environmental parameters, a principal component analysis with varimax rotation was performed for data reduction using the FACTOR ANALYSIS module of Statistica 5.1 (Statsoft 1994). Similarities in species composition among sample plots were assessed using the Czekanowski-Dice– Sørensen similarity index for presence/absence (Krebs 1989) and the Steinhaus similarity measure for species abundances (Faith et al. 1987). Species counts are first standardised by dividing each value by the maximum abundance for that species in the data set (Legendre & Legendre 1998). The results were ordinated by non-metric multidimensional scaling (NMDS) using PC-ORD 3.15 for windows (McCune & Mefford 1997). One thousand Monte Carlo permutations were used to test significance of Kruskal stress reduction when an additional axis is added. To visualize the relationships among the plots, a minimum-length spanning tree was fitted on the NMDS-plot using NTSYS-pc 1.8 (Rohlf 1993). Bray–Curtis distance matrices (Beals 1984) were obtained for the habitat and soil data, and compared with the similarity matrix based on the Diptera composition with partial Mantel tests (Smouse et al. 1986) to test congruence between the matrices. The correlation between two ecological matrices is computed, controlling for a third matrix. If F, V, S contain the ecological distances for the flies, vegetation and soil variables, a matrix RF (RV ) is calculated that contains the residuals from linear regression of F (V) against S. The Mantel test calculated between RF and RV gives the correspondence between the fly and vegetation distances, controlling for soil distances. Significance of the association was tested by 1000 Monte Carlo permutations (using PC-ORD 3.15). Because the relationships between insect communities and their environment may be obscured by spatial autocorrelation (identification of spurious correlations), a matrix based on the actual distances between the plots was calculated and also compared with the other distance matrices. Results Fly composition and diversity Between late April and the end of December 1995, a total of 2331 specimens of 29 Sphaeroceridae species (3 from subfamily Copromyzinae, 26 from subfamily Limosininae) and 2 Lonchopteridae species were caught in the traps (Appendix I). The communities are strongly dominated by two species.
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De Bruyn et al. Table 1. Species composition and diversity indices of the 11 sample plots. Dune
Mosaic
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11
Species composition # Species # Unique species # Genera # Individuals
3 0 3 7
5 0 5 42
9 0 7 29
12 1 9 191
10 0 7 259
10 1 7 491
11 0 8 241
11 1 8 831
12 1 7 126
9 1 5 20
21 8 11 94
Diversity Shannon–Wiener Avalanche
0.796 0.898
0.914 1.003
1.581 1.591
1.249 1.247
1.144 1.369
0.894 0.996
1.147 1.185
0.907 1.108
1.252 1.261
1.891 1.615
2.475 1.699
Minilimosina vitripennis (57%) and Spelobia ochripes (30%) together account for 87% of the total catch. They also occur respectively at 11 and 10 of the sample plots. The distribution of the other species is more restricted. Highest species richness (21) is found in the mixed wood site (P11) (Table 1). Eight species are unique to this plot. The traps of the two plots on top of the dune caught only 3 (P1) and 5 (P2) species. The remaining plots contained between 9 and 12 species. The number of individuals per plot ranges from 7 (P1) to 831 (P8). Both sites on the dune top (P1 and P2) have the lowest Shannon–Wiener diversity (Table 1). This is not surprising because they also have by far the lowest species richness. Site P6 and P8 also have low diversity values although they contain the highest number of individuals. This is due to the strong dominance of M. vitripennis (P6: 68%; P8: 70%) and S. ochripes (P6: 25%; P8: 23%). In comparison, site P11 had the highest diversity measures (and highest species richness), and a low number of individuals. The most dominant species in site P11 is Spelobia parapusio, with only 23% of the catch at this site. In general, the avalanche index, which includes the taxonomic distance between species, shows the same pattern as the other diversity indices. Again P1, P2, P6 and P8 have the lowest diversity while P11 is the most diverse. However, compared with the other indices, the increase is less pronounced at higher values (Figure 1). As a result, the discriminatory ability between the samples, expressed as the coefficient of variation, decreases from 44.2% for species richness, over 39.24% for the Shannon–Wiener index to 21.43% for the avalanche index. Czekanowski-Dice–Sørensen similarities (presence/ absence) based on the fly communities, range between 0.14 (P1–P11) and 0.90 (P5–P6). For the latter, the same species are found in both habitats except for Kimosina sp. in P5 and Coproica acutangula in P6. On average the similarity between two plots is 0.51
Figure 1. Relationship between the Shannon–Wiener and the avalanche index at the 11 sample sites (line: y = 2.004 exp(−0.569 x −1.5 ); r 2 = 0.92).
(median: 0.50). The Steinhaus similarities give the same pattern but the similarities between the plots are much lower and range from 0.006 (P1–P11) to 0.59 (P5–P6). Here the mean similarity is 0.28 (median: 0.23). The variation between the plots can adequately be depicted in three dimensions. A nonmetric 3-dimensional scaling plot has low stress (Kruskal stress, Czekanowski-Dice–Sørensen: 2.29; Steiner: 4.51). Increasing the number of dimensions does not decrease stress further (p > 0.05). The 11 fly communities fall in 4 groups (Figure 2). The first group consists of the three plots on the top of the dune (P1, P2, P3). A second group (P10, P11) consists of the plot near the fen (P10) and the wood plot (P11). Group three comprises plots P4 and P7. The last group consists of the remaining plots (P5, P6, P8, P9). Relationship with environment The vegetation of the sample plots consists of typical heathland communities. On dryer sites (e.g. P1, P2,
Relationship between vegetation, soil and fly diversity P3) Calluna vulgaris covers most of the plot. On wetter sites, Erica tetralix dominates the vegetation (e.g. plots P4, P6). On several plots, the heath plants are replaced by Molinia caerulea (e.g. plots P5, P7, P8, P11). In extremely wet plots (P9, P10), large patches of Sphagnum are found. Trees (Betula pendula, Pinus sylvestris, Quercus robur) are mainly found in plot P11. The other plants occur at low densities and are scattered among the different plots. The soil is very acidic (Table 2) and has a rather narrow pH range (between 3.73 and 4.82). Soil moisture varied between 4.5 and 93.3 ml/100 g wet mass. Organic matter ranges from 21 to 923 mg carbon per gram soil. Soil pH, moisture and organic matter are jointly independent (all r < 0.38, p > 0.255).
Figure 2. Non-metric three-dimensional scaling plot of the 11 sample plots. Lines represent edges of the minimal spanning tree from Czekanowski-Dice–Sørensen similarities.
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The Poisson regression reveals that two variables, soil moisture and organic matter content, influence species richness of the fly communities (Table 3, Figure 3a). Species richness increases when the soil contains a higher amount of organic matter. Soil moisture has a parabolic relationship. On dry soils, species richness increases when soils contain more water. When the soils become too wet, species richness decreases again. The same two variables remain in the linear regression model when species diversity is the dependent factor (F2,8 = 7.96; p = 0.012; R 2 = 0.66), although the relationship is less pronounced (Figure 3b). Again diversity increases when the organic matter content is higher in the soil; while soil moisture has a parabolic relationship. For the avalanche diversity index, only organic matter content remains in the regression model (AI = 1.114 + 0.0005 organic matter; F1,9 = 5.72; p = 0.04; R2 = 0.39). Some of the habitat structure variables (Table 2) are significantly correlated. Therefore we used a principal component analysis to reduce variable space. Three PC axes are extracted that explain 87% of the total variance (Table 4). The first PC axis is negatively correlated with the amount of bare ground, and positively correlated with herb and dry moss layer cover and is a measure of ground cover. The second PC axis correlates positively with tree layer cover and litter thickness. The third PC axis is negatively correlated with the bush layer cover and the cover by the Spagnum mosses. These three axes are used in the regression models to explain species richness and species diversity. PCs 1 and 2 remain in the Poisson regression model explaining species richness (Table 5, Figure 4). More species are found in habitats with a high ground cover (few or no open spaces), covered by trees, and with a thick litter layer (removing the 2 outliers in Figure 4
Table 2. Environmental variables for the 11 sample plots. Dune
Soil variables pH Moisture (ml/100 g wet mass) Organic matter (mg C/g dry mass) Habitat structure (% cover) Moss layer Herb layer Shrub layer Tree layer Litter thickness (mm)
Mosaic
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11
4.82 4.5 20.89
4.18 13.3 62.81
3.91 22.1 103.07
4.06 42.5 102.64
4.21 57.0 270.90
4.13 62.6 189.29
4.14 44.2 94.36
4.50 40.6 167.72
3.88 89.0 922.67
4.77 93.3 887.80
3.73 58.8 852.79
24 26 0 0 0
4 75 0 0 5
2 85 0 0 5
0 100 1 0 75
0 88 0 0 50
1 94 0 0 30
2 96 1 0 75
0 90 1 0 34
22 80 67 0 80
79 65 15 0 0
1 95 0 100 144
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does not change the coefficients: #species = exp(2.190 + 0.447 PC1 + 0.223 PC2)). Shannon– Wiener species diversity is only positively related to PC2 (Y = 1.295 + 0.343 PC2; F1,9 =7.518; p = 0.023; R 2 = 0.40). None of the three PC axes are significantly correlated with the avalanche diversity index (all p > 0.192). Fly species richness is negatively correlated with herb species richness (r = −0.62, p = 0.044), and uncorrelated with dry moss, Sphagnum, bush, tree or total species richness (all |r| < 0.52; p > 0.100). There is also no correlation between the vegetation diversity and the Shannon or avalanche index (all |r| < 0.30; p > 0.203). The habitat structure variables are also related to the soil variables. PC factor 1 is correlated with the amount of organic matter and soil moisture. The cover of the herb and moss layer rises sharply when soil moisture increases until a plateau phase is reached at about 15 ml per 100 g wet mass (Figure 5a: F4,7 = 100.286; Table 3. Soil factors influencing species richness. Poisson regression, analysis of deviance table. Effects
df
deviance
p(χ 2 )
Maximal model Soil moisture (Soil moisture)2 (Organic matter)2 Null model
7 1 1 1 10
0.93 13.56 13.75 5.50 20.071
